Status: Archive (code is provided as-is, no updates expected)
Code for "Jukebox: A Generative Model for Music"
# Required: Sampling
conda create --name jukebox python=3.7.5
conda activate jukebox
conda install mpi4py=3.0.3
conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.0 -c pytorch
git clone https://github.com/openai/jukebox.git
cd jukebox
pip install -r requirements.txt
pip install -e .
# Required: Training
conda install av=7.0.01 -c conda-forge
pip install ./tensorboardX
# Optional: Apex for faster training with fused_adam
conda install pytorch=1.1 torchvision=0.3 cudatoolkit=10.0 -c pytorch
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./apex
To sample normally, run the following command. Model can be 5b, 5b_lyrics, 1b_lyrics
python jukebox/sample.py --model=5b_lyrics --name=sample_5b --levels=3 --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125
python jukebox/sample.py --model=1b_lyrics --name=sample_1b --levels=3 --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 --sr=44100 --n_samples=16 --hop_fraction=0.5,0.5,0.125
The above generates the first sample_length_in_seconds seconds of audio from a song of total length total_sample_length_in_seconds.
The samples decoded from each level are stored in {name}/level_{level}.
You can also view the samples as an html with the aligned lyrics under {name}/level_{level}/index.html. Run python -m http.server and open the html through the server to see the lyrics animate as the song plays.
The hps are for a V100 GPU with 16 GB GPU memory. The 1b_lyrics, 5b, and 5b_lyrics top-level priors take up 3.8 GB, 10.3 GB, and 11.5 GB, respectively. The peak memory usage to store transformer key, value cache is about 400 MB for 1b_lyrics and 1 GB for 5b_lyrics per sample. If you are having trouble with CUDA OOM issues, try 1b_lyrics or decrease max_batch_size in sample.py, and --n_samples in the script call.
On a V100, it takes about 3 hrs to fully sample 20 seconds of music. Since this is a long time, it is recommended to use n_samples > 1 so you can generate as many samples as possible in parallel. The 1B lyrics and upsamplers can process 16 samples at a time, while 5B can fit only up to 3. Since the vast majority of time is spent on upsampling, we recommend using a multiple of 3 less than 16 like --n_samples 15 for 5b_lyrics. This will make the top-level generate samples in groups of three while upsampling is done in one pass.
If you want to prompt the model with your own creative piece or any other music, first save them as wave files and run
python jukebox/sample.py --model=5b_lyrics --name=sample_5b_prompted --levels=3 --mode=primed --audio_file=path/to/recording.wav,awesome-mix.wav,fav-song.wav,etc.wav --prompt_length_in_seconds=12 --sample_length_in_seconds=20 --total_sample_length_in_seconds=180 --sr=44100 --n_samples=6 --hop_fraction=0.5,0.5,0.125
This will load the four files, tile them to fill up to n_samples batch size, and prime the model with the first prompt_length_in_seconds seconds.
To train a small vqvae, run
mpiexec -n {ngpus} python jukebox/train.py --hps=small_vqvae --name=small_vqvae --sample_length=262144 --bs=4 --nworkers=4 --audio_files_dir={audio_files_dir} --labels=False --train --aug_shift --aug_blend
Here, {audio_files_dir} is the directory in which you can put the audio files for your dataset, and {ngpus} is number of GPU's you want to use to train.
The above trains a two-level VQ-VAE with downs_t = (5,3), and strides_t = (2, 2) meaning we downsample the audio by 2**5 = 32 to get the first level of codes, and 2**8 = 256 to get the second level codes.
Checkpoints are stored in the logs folder. You can monitor the training by running Tensorboard
tensorboard --logdir logs
Once the VQ-VAE is trained, we can restore it from its saved checkpoint and train priors on the learnt codes. To train the top-level prior, we can run
mpiexec -n {ngpus} python jukebox/train.py --hps=small_vqvae,small_prior,all_fp16,cpu_ema --name=small_prior --sample_length=2097152 --bs=4 --nworkers=4 --audio_files_dir={audio_files_dir} --labels=False --train --test --aug_shift --aug_blend --restore_vqvae=logs/small_vqvae/checkpoint_latest.pth.tar --prior --levels=2 --level=1 --weight_decay=0.01 --save_iters=1000
To train the upsampler, we can run
mpiexec -n {ngpus} python jukebox/train.py --hps=small_vqvae,small_upsampler,all_fp16,cpu_ema --name=small_upsampler --sample_length 262144 --bs 4 --nworkers 4 --audio_files_dir {audio_files_dir} --labels False --train --test --aug_shift --aug_blend --restore_vqvae logs/small_vqvae/checkpoint_latest.pth.tar --prior --levels 2 --level 0 --weight_decay 0.01 --save_iters 1000
We pass sample_length = n_ctx * downsample_of_level so that after downsampling the tokens match the n_ctx of the prior hps.
Here, n_ctx = 8192 and downsamples = (32, 256), giving sample_lengths = (8192 * 32, 8192 * 256) = (65536, 2097152) respectively for the bottom and top level.
Our pre-trained VQ-VAE can produce compressed codes for a wide variety of genres of music, and the pre-trained upsamplers can upsample them back to audio that sound very similar to the original audio. To re-use these for a new dataset of your choice, you can retrain just the top-level
To retrain top-level on a new dataset, run
mpiexec -n {ngpus} python jukebox/train.py --hps=vqvae,small_prior,all_fp16,cpu_ema --name=pretrained_vqvae_small_prior --sample_length=1048576 --bs=4 --nworkers=4 --bs_sample=4 --aug_shift --aug_blend --audio_files_dir={audio_files_dir} --labels=False --train --test --prior --levels=3 --level=2 --weight_decay=0.01 --save_iters=1000
You can then run sample.py with the top-level of our models replaced by your new model. To do so, add an entry my_model in MODELs (in make_models.py) with the (vqvae hps, upsampler hps, top-level prior hps) of your new model, and run sample.py with --model=my_model.
Please cite using the following bibtex entry:
@article{dhariwal2020jukebox,
title={Jukebox: A Generative Model for Music},
author={Dhariwal, Prafulla and Jun, Heewoo and Payne, Christine and Kim, Jong Wook and Radford, Alec and Sutskever, Ilya},
journal={arXiv preprint arXiv:[TODO]},
year={2020}
}